derivative-free-optimization

name	derivative-free-optimization
description	Optimization without gradient information
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"mathematics","domain":"science","category":"optimization","phase":6}
graph	{"domains":["domain:mathematics"],"specializations":["specialization:computational-mathematics"],"skillAreas":["skill-area:np-hard-heuristics","skill-area:mathematical-reasoning","skill-area:dynamic-programming"],"workflows":["workflow:experiment-design"],"roles":["role:computational-scientist","role:research-engineer"]}

Derivative-Free Optimization

Purpose

Provides optimization capabilities for problems where gradient information is unavailable or unreliable.

Capabilities

• Nelder-Mead simplex method
• Powell's method
• Surrogate-based optimization
• Bayesian optimization
• Pattern search methods
• Trust region methods

Usage Guidelines

1. Method Selection: Choose based on problem characteristics
2. Function Evaluations: Minimize expensive function calls
3. Surrogate Models: Build and refine surrogate approximations
4. Exploration-Exploitation: Balance search strategies

Tools/Libraries

• scipy.optimize
• Optuna
• GPyOpt